Data Simplification

Jules Berman holds two Bachelor of Science degrees from MIT (in Mathematics and in Earth and Planetary Sciences), a PhD from Temple University, and an MD from the University of Miami. He was a graduate researcher at the Fels Cancer Research Institute (Temple University) and at the American Health Foundation in Valhalla, New York. He completed his postdoctoral studies at the US National Institutes of Health, and his residency at the George Washington University Medical Center in Washington, DC. Dr. Berman served as Chief of anatomic pathology, surgical pathology, and cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, where he held joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. In 1998, he transferred to the US National Institutes of Health as a Medical Officer and as the Program Director for Pathology Informatics in the Cancer Diagnosis Program at the National Cancer Institute. Dr. Berman is a past President of the Association for Pathology Informatics and is the 2011 recipient of the Association’s Lifetime Achievement Award. He is a listed author of more than 200 scientific publications and has written more than a dozen books in his three areas of expertise: informatics, computer programming, and pathology. Dr. Berman is currently a freelance writer.

"As there is a "gold rush" encouraging the workforce training of data scientists, this gritty "Rules of the Road" monograph should serve as a constant companion for modern data scientists. Berman convincingly portrays the value of programmers and analysts who have facility with Perl, Python, or Ruby and who understand the critical role of metadata, indexing, and data visualization. These professionals will be high on my shopping list of talent to add to our biomedical informatics team in Pittsburgh.""Data Simplification provides easy, free solutions to the unintended consequences of data complexity. This book should be the first (and probably most important) guide to success in the data sciences. I will be providing copies to my trainees, programmers, analysts, and faculty, as required reading." --Michael J. Becich, MD, PhD, Associate Vice-Chancellor for Informatics in the Health Sciences, Chairman and Distinguished University Professor, Department of Biomedical Informatics, Director, Center for Commercial Application (CCA) of Healthcare Data, University of Pittsburgh School of Medicine

• 1. The Simple Life2. Structuring Text3. Indexing Text4. Understanding Your Data5. Identifying and Deidentifying Data6. Giving Meaning to Data7. Object-oriented data8. Problem simplification